Passing discrete feature along with Image in CNN model

I was performing a binary classification problem. Where each image has corresponding discreate features in an excel sheet. Currently, All I am doing passing the images thorugh a CNN mdoel and getiing the classification with soft max. However, I want to use those features(currenly not using in cnn) within my CNN model. This can be done adding those feautures in the fully connected layer, I guess.Or any other way.... But how to do that in mtalab? Is it possible to make the model end-to-end? Here is my current Model: [How to use features form outside adding with my CNN features]

layers = [
    imageInputLayer([32 32 3],"Name","imageinput")
    convolution2dLayer([3 3],512,"Name","conv1_1","WeightsInitializer","narrow-normal")
    tanhLayer("Name","tanh_1")
    maxPooling2dLayer([2 2],"Name","maxpool_2_2","Stride",[2 2])
    convolution2dLayer([5 5],256,"Name","conv1_2","Padding","same","WeightsInitializer","narrow-normal")
    tanhLayer("Name","tanh_2")
    maxPooling2dLayer([2 2],"Name","maxpool_2_1","Stride",[2 2])
    convolution2dLayer([7 7],128,"Name","conv1_3","Padding","same","WeightsInitializer","narrow-normal")
    reluLayer("Name","relu_3")
    maxPooling2dLayer([2 2],"Name","maxpool_3","Stride",[2 2])
    dropoutLayer(0.1,"Name","dropout")
    fullyConnectedLayer(2,"Name","fc","WeightsInitializer","narrow-normal") %Wanna add those features corresponding to each image.
    softmaxLayer("Name","softmax")
    classificationLayer("Name","classoutput")];